PertOrg 1.0: a comprehensive resource of multilevel alterations induced in model organisms by in vivo genetic perturbation. Issue Volume 51:Issue D1(2023) (16th October 2022)

• Record Type: Journal Article
• Authors: Zhai, Zhaoyu; Zhang, Xuelu; Zhou, Lu; Lin, Zhewei; Kuang, Ni; Li, Qiang; Ma, Qinfeng; Tao, Haodong; Gao, Jieya; Ma, Shiyong; Pan, Jianbo
• Is Part Of: Nucleic acids research. Volume 51:Issue D1(2023)
• Page Start: D1094
• Page End: D1101
• Publication Date: 2022-10-16
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkac872
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Genetically modified organisms (GMOs) can be generated to model human genetic disease or plant disease resistance, and they have contributed to the exploration and understanding of gene function, physiology, disease onset and drug target discovery. Here, PertOrg (http://www.inbirg.com/pertorg/ ) was introduced to provide multilevel alterations in GMOs. Raw data of 58 707 transcriptome profiles and associated information, such as phenotypic alterations, were collected and curated from studies involving in vivo genetic perturbation (e.g. knockdown, knockout and overexpression) in eight model organisms, including mouse, rat and zebrafish. The transcriptome profiles from before and after perturbation were organized into 10 116 comparison datasets, including 122 single-cell RNA-seq datasets. The raw data were checked and analysed using widely accepted and standardized pipelines to identify differentially expressed genes (DEGs) in perturbed organisms. As a result, 8 644 148 DEGs were identified and deposited as signatures of gene perturbations. Downstream functional enrichment analysis, cell type analysis and phenotypic alterations were also provided when available. Multiple search methods and analytical tools were created and implemented. Furthermore, case studies were presented to demonstrate how users can utilize the database. PertOrg 1.0 will be a valuable resource aiding in the exploration of gene functions, biological processes and disease models.